Zobrazeno 1 - 10
of 267
pro vyhledávání: '"Patil Kaustubh"'
Image Quality of MRI brain scans is strongly influenced by within scanner head movements and the resulting image artifacts alter derived measures like brain volume and cortical thickness. Automated image quality assessment is key to controlling for c
Externí odkaz:
http://arxiv.org/abs/2411.01268
Autor:
Nieto, Nicolás, Eickhoff, Simon B., Jung, Christian, Reuter, Martin, Diers, Kersten, Kelm, Malte, Lichtenberg, Artur, Raimondo, Federico, Patil, Kaustubh R.
Machine learning (ML) models benefit from large datasets. Collecting data in biomedical domains is costly and challenging, hence, combining datasets has become a common practice. However, datasets obtained under different conditions could present und
Externí odkaz:
http://arxiv.org/abs/2410.19643
Autor:
Luo, Xiaoliang, Rechardt, Akilles, Sun, Guangzhi, Nejad, Kevin K., Yáñez, Felipe, Yilmaz, Bati, Lee, Kangjoo, Cohen, Alexandra O., Borghesani, Valentina, Pashkov, Anton, Marinazzo, Daniele, Nicholas, Jonathan, Salatiello, Alessandro, Sucholutsky, Ilia, Minervini, Pasquale, Razavi, Sepehr, Rocca, Roberta, Yusifov, Elkhan, Okalova, Tereza, Gu, Nianlong, Ferianc, Martin, Khona, Mikail, Patil, Kaustubh R., Lee, Pui-Shee, Mata, Rui, Myers, Nicholas E., Bizley, Jennifer K, Musslick, Sebastian, Bilgin, Isil Poyraz, Niso, Guiomar, Ales, Justin M., Gaebler, Michael, Murty, N Apurva Ratan, Loued-Khenissi, Leyla, Behler, Anna, Hall, Chloe M., Dafflon, Jessica, Bao, Sherry Dongqi, Love, Bradley C.
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could pot
Externí odkaz:
http://arxiv.org/abs/2403.03230
Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirica
Externí odkaz:
http://arxiv.org/abs/2311.14079
Autor:
Sasse, Leonard, Nicolaisen-Sobesky, Eliana, Dukart, Juergen, Eickhoff, Simon B., Götz, Michael, Hamdan, Sami, Komeyer, Vera, Kulkarni, Abhijit, Lahnakoski, Juha, Love, Bradley C., Raimondo, Federico, Patil, Kaustubh R.
Machine learning (ML) provides powerful tools for predictive modeling. ML's popularity stems from the promise of sample-level prediction with applications across a variety of fields from physics and marketing to healthcare. However, if not properly i
Externí odkaz:
http://arxiv.org/abs/2311.04179
Autor:
Hamdan, Sami, More, Shammi, Sasse, Leonard, Komeyer, Vera, Patil, Kaustubh R., Raimondo, Federico
The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relati
Externí odkaz:
http://arxiv.org/abs/2310.12568
Autor:
Hamdan, Sami, Love, Bradley C., von Polier, Georg G., Weis, Susanne, Schwender, Holger, Eickhoff, Simon B., Patil, Kaustubh R.
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be removed as is commonly done by featurewise removal of their variance by li
Externí odkaz:
http://arxiv.org/abs/2210.09232
Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well as qualify whether it is meaningful for the population. Pearson's cor
Externí odkaz:
http://arxiv.org/abs/2208.07081
Autor:
Olfati, Mahnaz, Samea, Fateme, Faghihroohi, Shahrooz, Balajoo, Somayeh Maleki, Küppers, Vincent, Genon, Sarah, Patil, Kaustubh, Eickhoff, Simon B., Tahmasian, Masoud
Publikováno v:
In eBioMedicine October 2024 108
Autor:
Dagaev, Nikolay, Roads, Brett D., Luo, Xiaoliang, Barry, Daniel N., Patil, Kaustubh R., Love, Bradley C.
Despite their impressive performance in object recognition and other tasks under standard testing conditions, deep networks often fail to generalize to out-of-distribution (o.o.d.) samples. One cause for this shortcoming is that modern architectures
Externí odkaz:
http://arxiv.org/abs/2102.06406